Abstract:Cerebral blood vessels maintaining relatively constant cerebral blood flow (CBF) over wide range of systemic arterial blood pressure (ABP) is referred to as cerebral autoregulation (CA). Impairments in CA expose the brain to pressure-passive flow states leading to hypoperfusion and hyperperfusion. Cerebrovascular reactivity (CVR) metrics refer to surrogate metrics of pressure-based CA that evaluate the relationship between slow vasogenic fluctuations in cerebral perfusion pressure/ABP and surrogate for pulsati… Show more
“…Our reporting adhered to the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [ 15 ] and PRISMA Extension for Scoping Review [ 16 ]. The methodology and search approach employed in this review closely align with previous systematic reviews carried out by our research team [ 17 , 18 ]. The formulation of review objectives and the design of the search strategy were a collaborative effort involving the primary (NV, LF) and senior (FZ) authors.…”
The modeling and forecasting of cerebral pressure–flow dynamics in the time–frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure–flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.
“…Our reporting adhered to the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [ 15 ] and PRISMA Extension for Scoping Review [ 16 ]. The methodology and search approach employed in this review closely align with previous systematic reviews carried out by our research team [ 17 , 18 ]. The formulation of review objectives and the design of the search strategy were a collaborative effort involving the primary (NV, LF) and senior (FZ) authors.…”
The modeling and forecasting of cerebral pressure–flow dynamics in the time–frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure–flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.
“…We followed the guidelines outlined in the preferred reporting items for systematic reviews and meta-analysis (PRISMA) (Page et al 2020) and the PRISMA Extension for Scoping Review in our reporting (Tricco et al 2018). The methodology and search resembled those utilized in previous systematic reviews conducted by the research team (Gomez et al 2022, Sainbhi et al 2023. The collaborative efforts of the primary authors (AI and LF) and senior author (FAZ) formulated the review objectives and developed the search strategy.…”
Objective: Continuous monitoring of cerebrospinal compliance (CC)/ cerebrospinal compensatory reserve (CCR) is crucial for timely interventions and preventing more substantial deterioration in the context of acute neural injury, as it enables the early detection of abnormalities in intracranial pressure (ICP). However, to date, the literature on continuous CC/CCR monitoring is scattered and occasionally challenging to consolidate. 
Approach: 
We subsequently conducted a systematic scoping review of the human literature to highlight the available continuous CC/CCR monitoring methods.
Main Results:
This systematic review incorporated a total number of 76 studies, covering diverse patient types and focusing on three primary continuous CC or CCR monitoring metrics and methods – Moving Pearson’s correlation between ICP pulse amplitude waveform (AMP) and ICP, referred to as RAP, the Spiegelberg Compliance Monitor, changes in cerebral blood velocity (CBV) with respect to the alternation of ICP measured through Transcranial Doppler (TCD), changes in centroid metric, high frequency centroid (HFC) or higher harmonics centroid (HHC), and the P2/P1 ratio which are the distinct peaks of ICP pulse wave (ICPW). The majority of the studies in this review encompassed RAP metric analysis (n=43), followed by Spiegelberg Compliance Monitor (n=11), TCD studies (n=9), studies on the HFC/HHC (n=5), and studies on the P2/P1 ratio studies (n=6). These studies predominantly involved acute traumatic neural injury (i.e. Traumatic Brain Injury (TBI)) patients and those with hydrocephalus. RAP is the most extensively studied of the five focused methods and exhibits diverse applications. However, most papers lack clarification on its clinical applicability, a circumstance that is similarly observed for the other methods.
Significance: Future directions involve exploring RAP patterns and identifying characteristics and artifacts, investigating neuroimaging correlations with continuous CC/CCR and integrating machine learning, holding promise for simplifying CC/CCR determination. These approaches should aim to enhance the precision and accuracy of the metric, making it applicable in clinical practice.
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